A heating, ventilation and air conditioning (HVAC) system conditions the environment in a targeted space to a desired temperature and/or comfort level. HVAC systems range from a simple stove in a home environment to a complex air conditioning system in an airplane or submarine. An HVAC system is typically capable of offering some (if not total) control of temperature, humidity, ventilation, and/or filtration. The most common HVAC system is a building HVAC system, which keeps the indoor atmosphere comfortable for a home and/or workplace. Some HVAC systems offer “intelligent” features such as multiple programmable temperature set points based on time of day and/or day of the week to machine learning mechanisms that determine the occupant's habits. Almost all HVAC systems, however, require some input from end users to controls the desire operation of the HVAC system.
When choosing temperature setpoints for an HVAC system, an end user is typically balancing desires to both maintain a level of comfort and to minimize cost. With traditional HVAC systems, however, the end user cannot obtain a reliable estimate of the cost of chosen HVAC setpoints until after the fact when a utility bill arrives. Even adjusting HVAC setpoints based on the cost from a previous month may not be sufficiently predictive of changes in outdoor weather that may affect the amount of energy and cost required to obtain the adjusted HVAC setpoints.
A homeowner may be willing, for example, to spend $200 to keep an indoor temperature below 82° F. for a certain time period, but may not be willing to spend $250 to keep the indoor temperature below 80° F. for the same time period. Accordingly, in order to effectively balance comfort level and cost, there is a need for data-driven HVAC optimization that provides end users with an immediate cost estimate for HVAC setpoints.
It is difficult for a homeowner to predict the energy cost of an HVAC setpoint. Energy consumption is invisible to the end user and abstract. Additionally, energy may be priced dynamically by a local utility company based on time of day (e.g., “peak”, “off-peak”, “shoulder”, etc.). Traditional methods of estimating HVAC costs have significant drawbacks. Analytical modelling and software simulation, where mathematical equations are developed based on the physical attributes of a building (e.g., footprint, insulation material, location, orientation, etc.), require significant expertise to model complex systems. Furthermore, unique analytical models must be built for each unique building. Conventional data-driven modelling, including data mining and machine learning algorithms, is computationally expensive and is therefore unsuitable for implementation in a low cost system for broad public use.
Accordingly, there is a need for data-driven HVAC optimization that allows end users to make well-informed decisions by providing accurate medium term (e.g., 10-day) predictions for any building using low cost sensors, existing HVAC infrastructure, and available data.